The Modern Data Company

MAY 8, 2023

It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs. Potential downsides of data lakes include governance and integration challenges.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 10, 2023

To choose the most suitable data management solution for your organization, consider the following factors: Data types and formats: Do you primarily work with structured, unstructured, or semi-structured data? Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Data Lake Management Data Warehouse 52

The Modern Data Company

MAY 10, 2023

To choose the most suitable data management solution for your organization, consider the following factors: Data types and formats: Do you primarily work with structured, unstructured, or semi-structured data? Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

The Modern Data Company

MAY 10, 2023

To choose the most suitable data management solution for your organization, consider the following factors: Data types and formats: Do you primarily work with structured, unstructured, or semi-structured data? Consider whether you need a solution that supports one or multiple data formats.

Data Management 52

Data Management Management Data Lake Data Warehouse 52

Knowledge Hut

APRIL 23, 2024

Big Data vs Small Data: Function Variety Big Data encompasses diverse data types, including structured, unstructured, and semi-structured data. It involves handling data from various sources such as text documents, images, videos, social media posts, and more.

Big Data 52

Big Data Datasets Data Analysis Media 52

Knowledge Hut

APRIL 25, 2024

As businesses increasingly rely on intangible assets to create value, an efficient data management strategy is more important than ever. Data Integration Data integration is the process of combining information from several sources to give people a cohesive perspective.

Big Data 52

Big Data Data Analytics Manufacturing Retail 52

Ascend.io

FEBRUARY 28, 2024

Read More: AI Data Platform: Key Requirements for Fueling AI Initiatives How Data Engineering Enables AI Data engineering is the backbone of AI’s potential to transform industries , offering the essential infrastructure that powers AI algorithms.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Knowledge Hut

JANUARY 30, 2024

Goal To extract and transform data from its raw form into a structured format for analysis. To uncover hidden knowledge and meaningful patterns in data for decision-making. Data Source Typically starts with unprocessed or poorly structured data sources. Analyzing and deriving valuable insights from data.

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. Data integration , on the other hand, happens later in the data management flow.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Precisely

MARCH 9, 2023

A data warehouse implies a certain degree of preprocessing, or at the very least, an organized and well-defined data model. Data lakes, in contrast, are designed as repositories for all kinds of information, which might not initially be organized and structured.

Data Lake 59

Data Lake Data Warehouse Hadoop Raw Data 59

Knowledge Hut

OCTOBER 30, 2023

This data and reports are generated and developed by Power BI developers. A Power BI developer is a business intelligence personnel who thoroughly understands business intelligence, data integration, data warehousing, modeling, database administration, and technical aspects of BI systems.

BI 52

BI Business Intelligence Data Cleanse Business Analyst 52

Precisely

DECEMBER 19, 2022

Internal structured data alone is not sufficient – an incomplete database is ineffective. There’s immeasurable value in incorporating data enrichment into PropTech processes to drive better informed, data-driven decisions. However, real estate technology is only as good as the data that drives it.

Data Governance 52

Data Governance Retail Government High Quality Data 52

Knowledge Hut

MARCH 7, 2024

Data analytics tools in big data includes a variety of tools that can be used to enhance the data analysis process. These tools include data analysis, data purification, data mining, data visualization, data integration, data storage, and management. Integrate.io - Integrate.io

Big Data 52

Big Data Data Analytics Data Mining MongoDB 52

AltexSoft

MARCH 14, 2023

It must collect, analyze, and leverage large amounts of customer data from various sources, including booking history from a CRM system, search queries tracked with Google Analytics, and social media interactions. Okay, data lives everywhere, and that’s the problem the second component solves.

IT 59

IT Data Warehouse Data Governance Data Lake 59

phData: Data Engineering

SEPTEMBER 19, 2023

Data modeling methodologies are systematic approaches used to design and define the structure and relationships of data within a system. They provide a framework for organizing and representing data elements, attributes, and relationships. Want to learn more about data governance?

Data Lake 52

Data Lake Process Metadata Data Warehouse 52

DareData

JANUARY 30, 2023

As the field of data science and machine learning continues to evolve, it is increasingly evident that data engineering cannot be separated from it. Gone are the days when organizations could rely on models trained and stored in notebooks without any structure, governance, or testing.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Ascend.io

NOVEMBER 21, 2023

More importantly, we will contextualize ELT in the current scenario, where data is perpetually in motion, and the boundaries of innovation are constantly being redrawn. Extract The initial stage of the ELT process is the extraction of data from various source systems. What Is ELT? So, what exactly is ELT?

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Cloudera

OCTOBER 7, 2021

In this blog, I will demonstrate the value of Cloudera DataFlow (CDF) , the edge-to-cloud streaming data platform available on the Cloudera Data Platform (CDP) , as a Data integration and Democratization fabric. need to integrate multiple “point solutions” used in a data ecosystem) and organization reasons (e.g.,

Architecture 123

Architecture Metadata Government Kafka 123

Cloudera

SEPTEMBER 12, 2022

First, organizations have a tough time getting their arms around their data. More data is generated in ever wider varieties and in ever more locations. Organizations don’t know what they have anymore and so can’t fully capitalize on it — the majority of data generated goes unused in decision making.

Unstructured Data 73

Unstructured Data Data Lake Data Architecture Data 73

Knowledge Hut

JULY 26, 2023

This velocity aspect is particularly relevant in applications such as social media analytics, financial trading, and sensor data processing. Variety: Variety represents the diverse range of data types and formats encountered in Big Data. Handling this variety of data requires flexible data storage and processing methods.

Big Data 52

Big Data Data Cleanse Retail Healthcare 52

Knowledge Hut

APRIL 25, 2023

Data modeling: Data engineers should be able to design and develop data models that help represent complex data structures effectively. Data processing: Data engineers should know data processing frameworks like Apache Spark, Hadoop, or Kafka, which help process and analyze data at scale.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Monte Carlo

NOVEMBER 20, 2023

As your datasets start to grow beyond what can be manually inspected, there’s a transition to using dbt for more structured data testing. Column-level tests are manually added to validate data integrity and ensure specific data quality standards. Now it’s time to turn your attention outward.

Government 69

Government Data Data Governance Datasets 69

Knowledge Hut

JUNE 28, 2023

In our data-driven world, our lives are governed by big data. The TV shows we watch, the social media we follow, the news we read, and even the optimized routes we take to work are all influenced by the power of big data analytics. Focus Exploration and discovery of hidden patterns and trends in data.

Data Mining 52

Data Mining Business Intelligence BI Datasets 52

AltexSoft

SEPTEMBER 23, 2021

A data hub is a central mediation point between various data sources and data consumers. It’s not a single technology, but rather an architectural approach that unites storages, data integration and orchestration tools. An ETL approach in the DW is considered slow, as it ships data in portions (batches.)

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

Ascend.io

NOVEMBER 14, 2023

Moreover, they are the custodians of regulatory governance, and ensure that the organization’s AI capabilities comply with applicable industry standards. Data Management: Data is the lifeblood of AI, and the CAIO develops the comprehensive data strategy that aligns the company’s data executives to fuel its AI initiatives.

Pipeline-centric 52

Pipeline-centric Government Data Pipeline Recruitment 52

AltexSoft

MAY 12, 2023

What is unstructured data? Definition and examples Unstructured data , in its simplest form, refers to any data that does not have a pre-defined structure or organization. It can come in different forms, such as text documents, emails, images, videos, social media posts, sensor data, etc.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Knowledge Hut

MARCH 27, 2024

is an all-in-one solution for businesses to connect their data and applications. It offers a cloud-based integration platform that helps businesses automate their data integration processes. Google Fusion Tables Google Fusion Tables is a web service that allows users to upload, merge, and visualize data tables.

Big Data 52

Big Data Data Analytics MongoDB Big Data Tools 52

Precisely

SEPTEMBER 6, 2023

However, fewer than half of survey respondents rate their trust in data as “high” or “very high.” ” Poor data quality impedes the success of data programs, hampers data integration efforts, limits data integrity causing big data governance challenges.

Data Governance 52

Data Governance Big Data Government Unstructured Data 52

ProjectPro

JANUARY 27, 2023

Data Warehousing - ETL tools and processes can be leveraged to load data into a data warehouse for reporting and analysis. Master Data Management - ETL processes can be leveraged to maintain a single version of truth for key data entities by enforcing data governance, consolidation, and tracking data lineage.

BI 52

BI ETL Tools Retail Healthcare 52

Monte Carlo

AUGUST 25, 2023

Understanding data warehouses A data warehouse is a consolidated storage unit and processing hub for your data. Teams using a data warehouse usually leverage SQL queries for analytics use cases. This same structure aids in maintaining data quality and simplifies how users interact with and understand the data.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Monte Carlo

JULY 10, 2023

Incomplete data from external sources When you ingest data from an external source, you lack a certain amount of control over how data is structured and made available. Different sources may structure data inconsistently, which can lead to missing values within your data sets.

Data Collection 40

Data Collection Data Governance Government Data 40

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Scala 64

Scala Data Lake BI Google Cloud 64

ProjectPro

FEBRUARY 16, 2023

Data integration with ETL has evolved from structured data stores with high computing costs to natural state storage with read operation alterations thanks to the agility of the cloud. Data integration with ETL has changed in the last three decades. Q) Does AWS have an ETL Tool?

AWS 52

AWS Data Management ETL Tools Management 52

ProjectPro

AUGUST 11, 2021

This means that a data warehouse is a collection of technologies and components that are used to store data for some strategic use. Data is collected and stored in data warehouses from multiple sources to provide insights into business data. Data from data warehouses is queried using SQL.

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

Precisely

MAY 11, 2023

Traditional ERP systems are good at managing highly structured data. Read our eBook How to Solve the Top 10 Customer Master Data Challenges in SAP Learn more about SAP S/4HANA migration and the benefits of automation with Precisely. KPMG reported that C-level executives are investing heavily in technology focused on automation.

Data Cleanse 52

Data Cleanse Management Systems Government 52

AltexSoft

NOVEMBER 10, 2021

The data in this case is checked against the pre-defined schema (internal database format) when being uploaded, which is known as the schema-on-write approach. Purpose-built, data warehouses allow for making complex queries on structured data via SQL (Structured Query Language) and getting results fast for business intelligence.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

AltexSoft

AUGUST 22, 2022

The self-service functionally allows the entire organization to find relevant data faster and gain valuable insights. Support for different data types and use cases. A data fabric supports structured, unstructured, and semi-structured data whether it comes in real-time or generated in batches.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52